Computational Intelligence in Economics and Finance: Volume II, 2. sējumsPaul P. Wang, Tzu-Wen Kuo Springer Science & Business Media, 2007. gada 11. jūl. - 228 lappuses Computational intelligence (CI), as an alternative to statistical and econometric approaches, has been applied to a wide range of economics and finance problems in recent years, for example to price forecasting and market efficiency. This book contains research ranging from applications in financial markets and business administration to various economics problems. Not only are empirical studies utilizing various CI algorithms presented, but so also are theoretical models based on computational methods. In addition to direct applications of computational intelligence, readers can also observe how these methods are combined with conventional analytical methods such as statistical and econometric models to yield preferred results. Chen, Wang, and Kuo have grouped the 12 contributions following their introductory chapter into applications of fuzzy logic, neural networks (including self-organizing maps and support vector machines), and evolutionary computation. All chapters were selected either by invitation or based on a careful selection and extension of best papers from the International Workshop on Computational Intelligence in Economics and Finance in 2005. Overall, the book offers researchers an excellent overview of current advances and applications of computational intelligence techniques to economics and finance problems. |
No grāmatas satura
1.–5. rezultāts no 20.
... number of research efforts that utilizes computational intelligence techniques when investigating market efficiency ... fuzzy logic, neural networks, nonlinear principal components analysis, k-mean clustering, instant-based techniques ...
... fuzzy logic, neural networks (including self-organized maps and support vector machines) and evolutionary ... number of techniques which can be seen in Vol. 1 are not presented here, including rough sets, wavelets, swarm intelligence ...
Volume II Paul P. Wang, Tzu-Wen Kuo. 3. Fuzzy. Logic. Fuzzy logic interests us ... number of features. In this way, a formal decision rule can be explicitly ... Fuzzy numbers, fuzzy coefficients (in the fuzzy regression models) or fuzzy ...
Volume II Paul P. Wang, Tzu-Wen Kuo. from fuzzy sets and fuzzy numbers, the author concisely goes through fuzzy ... number of studies that artificial neural networks, as representative of a more general class of non-linear models, can ...
... number of correlated attributes, and hence a large number of redundancies. It is, therefore, a natural attempt to ... Fuzzy Logic. 8 S.-H. Chen et al.
Saturs
1 | |
An Overview of Insurance Uses of Fuzzy Logic | 24 |
ArnoldF Shapiro 25 | 63 |
Estimating Female Labor Force Participation through Statistical | 93 |
An Application of Kohonens SOFM to the Management | 106 |
Trading Strategies Based on Kmeans Clustering and Regression Models | 123 |
Application of an Instance Based Learning Algorithm for Predicting | 144 |
Nonlinear GoalDirected CPPI Strategy | 183 |
A LogicalHeuristic Approach | 209 |
Index | 224 |
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Computational Intelligence in Economics and Finance: Volume II Paul P. Wang,Tzu-Wen Kuo Priekšskatījums nav pieejams - 2010 |
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